利用LISA从味解构模型中重建随机引力波信号
Reconstructing stochastic gravitational-wave signals from flavour deconstruction with LISA
- Universität Zürich（苏黎世大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究利用LISA对味解构模型中一阶相变产生的随机引力波背景进行重建，采用SGWBinner联合重建信号、噪声与前景，发现强信号可成功重建，弱信号与河外致密双星前景简并，严格先验信息可改善重建效果。
AI中文摘要：
我们利用激光干涉空间天线（LISA）研究味解构模型中一阶相变产生的随机引力波背景的重建问题。在这些场景中，TeV能标下扩展的味非通用规范对称性的自发破缺，既可以产生观测到的标准模型费米子质量层级和混合角，又能诱导强一阶相变。我们选取该模型的代表性基准点，计算对应的热力学相变参数，并采用最先进的声波模板构建相应的引力波谱。随后将这些谱注入模拟的LISA数据中，使用SGWBinner进行贝叶斯推断，联合重建宇宙学信号、仪器噪声和天体物理前景。我们发现最强的基准信号可被成功重建，而较弱的信号与未分辨的河外致密双星前景存在显著简并。我们进一步表明，基于地面探测器观测得到的该前景的更严格先验信息，可降低这种简并并改善信号重建。我们的结果证明味解构模型可产生LISA可探测的信号，同时凸显了天体物理前景建模对其识别和表征的重要性。
英文摘要：
We investigate the reconstruction of a stochastic gravitational-wave background generated by a first-order phase transition in flavour-deconstruction models using the Laser Interferometer Space Antenna (LISA). In these scenarios, the spontaneous breaking of an extended flavour-non-universal gauge symmetry at the TeV scale can both generate the observed hierarchies of Standard Model fermion masses and mixing angles and induce a strong first-order phase transition. We consider representative benchmark points of the model, compute the corresponding thermodynamic transition parameters, and construct the resulting gravitational-wave spectra using a state-of-the-art sound-wave template. We then inject these spectra into simulated LISA data and perform Bayesian inference with SGWBinner, jointly reconstructing the cosmological signal, instrumental noise, and astrophysical foregrounds. We find that the strongest benchmark signal can be successfully reconstructed, whereas weaker signals are substantially degenerate with the unresolved extragalactic compact-binary foreground. We further show that tighter prior information on this foreground, motivated by observations with ground-based detectors, can reduce this degeneracy and improve signal reconstruction. Our results demonstrate that flavour-deconstruction models can produce signals accessible to LISA, while highlighting the importance of astrophysical-foreground modelling for their identification and characterization.
